Rapid and brief communication: Design efficient support vector machine for fast classification

Authors:
Yiqiang Zhan;Dinggang Shen
Affiliations:
Department of Computer Science, The Johns Hopkins University, Baltimore, MD, USA and Center for Computer-Integrated Surgical Systems and Technology, The Johns Hopkins University, Baltimore, MD, US ...;Center for Computer-Integrated Surgical Systems and Technology, The Johns Hopkins University, Baltimore, MD, USA and Section of Biomedical Image Analysis, Department of Radiology, University of Pe ...
Venue:
Pattern Recognition
Year:
2005

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Abstract

This paper presents a four-step training method for increasing the efficiency of support vector machine (SVM). First, a SVM is initially trained by all the training samples, thereby producing a number of support vectors. Second, the support vectors, which make the hypersurface highly convoluted, are excluded from the training set. Third, the SVM is re-trained only by the remaining samples in the training set. Finally, the complexity of the trained SVM is further reduced by approximating the separation hypersurface with a subset of the support vectors. Compared to the initially trained SVM by all samples, the efficiency of the finally-trained SVM is highly improved, without system degradation.